This dataset contains shipping emissions modelled in EMERGE Project (https://emerge-h2020.eu/) by FMI using STEAM model. The dataset contains current total annual emissions per grid cell for 2018 and modelled to 2050 with following EMERGE Scenarios: 2050 Scenario 3: Scenario 3 is a high-pressure scenario. The maritime transport development is High, there are no further measures to reduce the use of fossil fuels in shipping other than those already decided today, there is High use of scrubbers and high use of SCR. In this scenario there is thus a significant pressure on the environment from scrubber water and also high emissions of NH3 from the use of SCR as well as high emissions of CO2. SECA and NECA are introduced in all European sea areas. 2050 Scenario 8: Scenario 8 is an LNG scenario. High development in ship traffic is assumed, measures are in place to reach the IMO 50% goal, low use of scrubbers, and low use of SCR. This dataset is a collection for raster files outputted from STEAM NetCDF outputs. Dataset contains air emissions of CO, CO2, CH4, PM2.5, SOx and VOC. Unit of air emissions is kg per raster cell. Air emissions are calculated for 2018 and 2050 Scenarios 3 and 8. Dataset contains discharge volumes of stern tube oil, sewage, sewage nitrogen, open loop scrubber effluets, closed loop scrubber effluents, grey water, bilge water and ballast water. Since Scenarios 3 and 8 do not differ what comes to , Scenario 3 is used. Units of discharge volumes are following: Open loop scrubber effluent; liters Closed loop scrubber effluent; liters Bilge water; liters Grey water; liters Sewage; liters Stern tube oil; liters Ballast water; ton (=1000kg) Sewage Nitrogen; grams Resolution of the rasters is 0.05° x 0.1° For further information on methodology, see: Jalkanen, J. P., A. Brink, J. Kalli, H. Pettersson, J. Kukkonen, and T. Stipa. 2009. A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area. Atmos. Chem. Phys. 9:9209-9223. Jalkanen, J. P., L. Johansson, J. Kukkonen, A. Brink, J. Kalli, and T. Stipa. 2012. Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide. Atmos. Chem. Phys. 12:2641-2659. Johansson, L., J.-P. Jalkanen, and J. Kukkonen. 2017. Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution. Atmospheric Environment 167:403-415.

This dataset contains deposition of substances resulting from shipping modelled in EMERGE Project (https://emerge-h2020.eu/) by FMI using SILAM model. The model output is annual mean deposition rate in mg/(m2*year) per raster cell. Shipping contribution was calculated from the difference of two model runs, one with shipping emissions, and other one without the shipping contribution. Both dry and wet deposition was taken into account. For nitrogen containing substances, deposition mass is mass of Nitrogen (N) within the substances. For sulphates, deposition mass is mass of Sulphur (S) within the substances. For PM2.5, Cd, Pb deposition mass is total mass of the substance in question. Nitrogen containing substances consists of SILAM model species: NO, NO2, Aerosol nitrates (NH4NO3 and coarse nitrates NO3_c), NH3, HNO3, HONO, N2O5, HNO4 (PNA), Peroxyacetyl nitrate (PAN), C3 and higher peroxyacyl nitrates (PANX), organic nitrates (NTR), (NH4)1.5SO4. Other substances covered in deposition modelling outputs are SO4, PM2.5, Lead and Cadmium. Resolution of the original SILAM dataset is 0.05 x 0.05 degrees in lat-lon coordinate-system (WGS84). The cite to the source dataset is the following: Hänninen, R., Sofiev, M., Uppstu, A., Kouznetsov, R., and Palamarchuk, J. (2024). Shipping contribution to European air quality and depositions, both in 2018 and in two future scenarios for 2050, modelled by SILAM CTM [Data set]. Finnish Meteorological Institute. https://doi.org/10.57707/FMI-B2SHARE.6220D9EFFBA84B6E8EF9BF497AA62041 For further information on methodology, see e.g. EMERGE reports D5.3 and D5.4 The dataset contains current total annual deposition per grid cell calculated for 2018 and change compared between 2018 and modelled EMERGE Scenarios 3 and 8 for year 2050. Thus, negative raster values in Scenarios 3 or 8 is resulting from decrease of deposition according to SILAM model outputs. Scenario descriptions: 2050 Scenario 3: Scenario 3 is a high-pressure scenario. The maritime transport development is High, there are no further measures to reduce the use of fossil fuels in shipping other than those already decided today, there is High use of scrubbers and high use of SCR. In this scenario there is thus a significant pressure on the environment from scrubber water and also high emissions of NH3 from the use of SCR as well as high emissions of CO2. SECA and NECA are introduced in all European sea areas. 2050 Scenario 8: Scenario 8 is an LNG scenario. High development in ship traffic is assumed, measures are in place to reach the IMO 50% goal, low use of scrubbers, and low use of SCR. This dataset is a collection for raster files outputted from SILAM NetCDF outputs. For 2018 emissions, deposition from shipping per grid cell is used. For 2050, change from 2018 is described in maps. Thus negative values induce reductions in deposition.

The dataset contains data on bridges and other constructions. The dataset is constructed from Open Street Map “roads” shapefiles downloaded through Geofabrik by extracting all features where attribute bridges=1. It should be noted that the dataset contains major bridges and all other smaller constructions that have been classified as bridges in Open Street Map. The coverage for the dataset is whole Baltic.

This dataset includes digitized deep-water route, traffic separation schemes, precautionary areas and inshore traffic zones in the Baltic Sea as defined in the 9th edition of the Ships Routing Guide (2008) of the International Maritime Organization. It was updated in 2017. The update includes information about new traffic separation schemes and deep-water routes, amendments to the existing traffic separation schemes and establishment of new two-way routes which are not included in the original 2008 routeing guide, but were adopted by the 54th, 55th, 57th, and 58th session of the Sub-Committee on the Safety of Navigation of the IMO, and 3rd session of IMO's Sub-Committee Meeting on the Navigation, Communications, Search and Rescue.

A vector grid in 2 x 2 km resolution showing model results of environmental impact caused by spill of soluble oil from ships with size less than 5000 t as as g oil / km^2 weighted.This dataset has been produced by COWI (http://www.cowi.dk) for the BRISK project (Sub-regional risk of spill of oil and hazardous substances in the Baltic Sea, http://www.brisk.helcom.fi/). Fields: COL_NO (Dbl): Column ROW_NO (Dbl): Row WLoad (Dbl): Environmental impact (g oil / km^2 weighted).

This dataset depicts risk of oil spill from collisions at intersections. The modeled risk is calculated for the years 2008/2009. The area of the bubbles corresponds to the risk of spill of oil and hazardous substances. The unit of the risk is average tonnes per year. This dataset has been produced by Albrecht Lentz, COWI (http://www.cowi.dk) for the BRISK project (Sub-regional risk of spill of oil and hazardous substances in the Baltic Sea, http://www.brisk.helcom.fi/). The dataset is a model result from a software code owned and operated by COWI. BRISK and BRISK-RU provide information on spatial distribution of risks of pollution from ships in the six sub-regions of the Baltic Sea, according to different types of accidents and spill sizes. The assessment takes into account the existing risk control measures as well as the prognosis for future maritime traffic. Groundings and ship-to-ship collisions are by far the most likely types of accidents resulting in pollution. Other kinds of incidents, such as fire, collisions with fixed objects, spills from offshore platforms, as well as illegal discharges have minor contribution to the risks. Further, the oil impact has been modelled. The oil impact can be described as the amount of spilled oil that is expected on the sea surface. The effects of oil drift, weathering and fate, as well as the oil recovery are taken into account. Field descriptions: LON: Longitude (center of ellipse) LAT: Latitude (center of ellipse) SPILLALL: Risk [average tonnes per year], sum of all spills. Used for visualization. SPILL12: Risk [average tonnes per year], small size spills. SPILL34: Risk [average tonnes per year], medium size spills. SPILL123: Risk [average tonnes per year], small & medium size spills. SPILL4: Risk [average tonnes per year], medium size spills. SPILL1234: Risk [average tonnes per year], small & medium size spills. SPILL567: Risk [average tonnes per year] large spills.

This dataset depicts risk of oil spill from illegal spills. The modeled risk is calculated for the scenario year 2020 based on predicted shipping traffic density. The area of the bubbles corresponds to the risk of spill of oil and hazardous substances. The unit of the risk is average tonnes per year. This dataset has been produced by Albrecht Lentz, COWI (http://www.cowi.dk) for the BRISK project (Sub-regional risk of spill of oil and hazardous substances in the Baltic Sea, http://www.brisk.helcom.fi/). The dataset is a model result from a software code owned and operated by COWI. BRISK and BRISK-RU provide information on spatial distribution of risks of pollution from ships in the six sub-regions of the Baltic Sea, according to different types of accidents and spill sizes. The assessment takes into account the existing risk control measures as well as the prognosis for future maritime traffic. Groundings and ship-to-ship collisions are by far the most likely types of accidents resulting in pollution. Other kinds of incidents, such as fire, collisions with fixed objects, spills from offshore platforms, as well as illegal discharges have minor contribution to the risks. Further, the oil impact has been modelled. The oil impact can be described as the amount of spilled oil that is expected on the sea surface. The effects of oil drift, weathering and fate, as well as the oil recovery are taken into account. Field descriptions: LON: Longitude (center of ellipse) LAT: Latitude (center of ellipse) SPILLALL: Risk [average tonnes per year], sum of all spills. Used for visualization. SPILL12: Risk [average tonnes per year], small size spills. SPILL34: Risk [average tonnes per year], medium size spills. SPILL123: Risk [average tonnes per year], small & medium size spills. SPILL4: Risk [average tonnes per year], medium size spills. SPILL1234: Risk [average tonnes per year], small & medium size spills. SPILL567: Risk [average tonnes per year] large spills.

This dataset depicts risk of oil spills from collision with fixed objects and spills from offshore platforms, terminals, bunkering and STS operation. The modeled risk is calculated for the years 2008/2009. The area of the bubbles corresponds to the risk of spill of oil and hazardous substances. The unit of the risk is average tonnes per year. This dataset has been produced by Albrecht Lentz, COWI (http://www.cowi.dk) for the BRISK project (Sub-regional risk of spill of oil and hazardous substances in the Baltic Sea, http://www.brisk.helcom.fi/). The dataset is a model result from a software code owned and operated by COWI. BRISK and BRISK-RU provide information on spatial distribution of risks of pollution from ships in the six sub-regions of the Baltic Sea, according to different types of accidents and spill sizes. The assessment takes into account the existing risk control measures as well as the prognosis for future maritime traffic. Groundings and ship-to-ship collisions are by far the most likely types of accidents resulting in pollution. Other kinds of incidents, such as fire, collisions with fixed objects, spills from offshore platforms, as well as illegal discharges have minor contribution to the risks. Further, the oil impact has been modelled. The oil impact can be described as the amount of spilled oil that is expected on the sea surface. The effects of oil drift, weathering and fate, as well as the oil recovery are taken into account. Field descriptions: LON: Longitude (center of ellipse) LAT: Latitude (center of ellipse) SPILLALL: Risk [average tonnes per year], sum of all spills. Used for visualization. SPILL12: Risk [average tonnes per year], small size spills. SPILL34: Risk [average tonnes per year], medium size spills. SPILL123: Risk [average tonnes per year], small & medium size spills. SPILL4: Risk [average tonnes per year], medium size spills. SPILL1234: Risk [average tonnes per year], small & medium size spills. SPILL567: Risk [average tonnes per year] large spills.

A vector grid in 2 x 2 km resolution showing model results of environmental damage caused by spill of soluble oil from ships with size less than 5000 t as incidents/million years weighted.This dataset has been produced by COWI (http://www.cowi.dk) for the BRISK project (Sub-regional risk of spill of oil and hazardous substances in the Baltic Sea, http://www.brisk.helcom.fi/). Fields: COL_NO (Dbl): Column ROW_NO (Dbl): Row WLoad (Dbl): Environmental damage (Incidents/million years weighted).

A vector grid in 2 x 2 km resolution showing model results of environmental damage caused by spill of soluble oil from ships of all sizes as incidents/million years weighted.This dataset has been produced by COWI (http://www.cowi.dk) for the BRISK project (Sub-regional risk of spill of oil and hazardous substances in the Baltic Sea, http://www.brisk.helcom.fi/). Fields: COL_NO (Dbl): Column ROW_NO (Dbl): Row WLoad (Dbl): Environmental damage (Incidents/million years weighted).